=============== <Original Dataset> =============== <class 'pandas.core.frame.DataFrame'> RangeIndex: 20640 entries, 0 to 20639 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 longitude 20640 non-null float64 1 latitude 20640 non-null float64 2 housing_median_age 20640 non-null float64 3 total_rooms 20640 non-null float64 4 total_bedrooms 20433 non-null float64 5 population 20640 non-null float64 6 households 20640 non-null float64 7 median_income 20640 non-null float64 8 median_house_value 20640 non-null float64 9 ocean_proximity 20640 non-null object dtypes: float64(9), object(1) memory usage: 1.6+ MB None
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | 452600.0 | NEAR BAY |
| 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | 358500.0 | NEAR BAY |
| 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | 352100.0 | NEAR BAY |
| 3 | -122.25 | 37.85 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | 341300.0 | NEAR BAY |
| 4 | -122.25 | 37.85 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | 342200.0 | NEAR BAY |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20635 | -121.09 | 39.48 | 25.0 | 1665.0 | 374.0 | 845.0 | 330.0 | 1.5603 | 78100.0 | INLAND |
| 20636 | -121.21 | 39.49 | 18.0 | 697.0 | 150.0 | 356.0 | 114.0 | 2.5568 | 77100.0 | INLAND |
| 20637 | -121.22 | 39.43 | 17.0 | 2254.0 | 485.0 | 1007.0 | 433.0 | 1.7000 | 92300.0 | INLAND |
| 20638 | -121.32 | 39.43 | 18.0 | 1860.0 | 409.0 | 741.0 | 349.0 | 1.8672 | 84700.0 | INLAND |
| 20639 | -121.24 | 39.37 | 16.0 | 2785.0 | 616.0 | 1387.0 | 530.0 | 2.3886 | 89400.0 | INLAND |
20640 rows × 10 columns
=============== <Modified Dataset> =============== <class 'pandas.core.frame.DataFrame'> RangeIndex: 20433 entries, 0 to 20432 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 longitude 20433 non-null float64 1 latitude 20433 non-null float64 2 housing_median_age 20433 non-null float64 3 total_rooms 20433 non-null float64 4 total_bedrooms 20433 non-null float64 5 population 20433 non-null float64 6 households 20433 non-null float64 7 median_income 20433 non-null float64 8 ocean_proximity 20433 non-null object dtypes: float64(8), object(1) memory usage: 1.4+ MB None
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | ocean_proximity | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | -122.23 | 37.88 | 41.0 | 880.0 | 129.0 | 322.0 | 126.0 | 8.3252 | NEAR BAY |
| 1 | -122.22 | 37.86 | 21.0 | 7099.0 | 1106.0 | 2401.0 | 1138.0 | 8.3014 | NEAR BAY |
| 2 | -122.24 | 37.85 | 52.0 | 1467.0 | 190.0 | 496.0 | 177.0 | 7.2574 | NEAR BAY |
| 3 | -122.25 | 37.85 | 52.0 | 1274.0 | 235.0 | 558.0 | 219.0 | 5.6431 | NEAR BAY |
| 4 | -122.25 | 37.85 | 52.0 | 1627.0 | 280.0 | 565.0 | 259.0 | 3.8462 | NEAR BAY |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20428 | -121.09 | 39.48 | 25.0 | 1665.0 | 374.0 | 845.0 | 330.0 | 1.5603 | INLAND |
| 20429 | -121.21 | 39.49 | 18.0 | 697.0 | 150.0 | 356.0 | 114.0 | 2.5568 | INLAND |
| 20430 | -121.22 | 39.43 | 17.0 | 2254.0 | 485.0 | 1007.0 | 433.0 | 1.7000 | INLAND |
| 20431 | -121.32 | 39.43 | 18.0 | 1860.0 | 409.0 | 741.0 | 349.0 | 1.8672 | INLAND |
| 20432 | -121.24 | 39.37 | 16.0 | 2785.0 | 616.0 | 1387.0 | 530.0 | 2.3886 | INLAND |
20433 rows × 9 columns
=============== AutoML Start =============== =============== Model : Mean shift =============== best bandwidth = 3.075515672213992 max_iter = 100 / bandwidth = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0 9012
1.0 8262
2.0 1739
3.0 109
4.0 70
...
56.0 2
57.0 1
58.0 263
59.0 51
60.0 55
Name: median_house_value, Length: 61, dtype: int64
===max===
predict
0.0 500001.0
1.0 500001.0
2.0 500001.0
3.0 500001.0
4.0 500001.0
...
56.0 343500.0
57.0 58600.0
58.0 500001.0
59.0 500001.0
60.0 500001.0
Name: median_house_value, Length: 61, dtype: float64
===median===
predict
0.0 185700.0
1.0 171700.0
2.0 166000.0
3.0 163300.0
4.0 166100.0
...
56.0 264450.0
57.0 58600.0
58.0 210300.0
59.0 340900.0
60.0 215800.0
Name: median_house_value, Length: 61, dtype: float64
===min===
predict
0.0 14999.0
1.0 14999.0
2.0 22500.0
3.0 53400.0
4.0 47500.0
...
56.0 185400.0
57.0 58600.0
58.0 52800.0
59.0 40000.0
60.0 67900.0
Name: median_house_value, Length: 61, dtype: float64
===mean===
predict
0.0 215848.624723
1.0 198483.938635
2.0 197666.579068
3.0 196085.357798
4.0 188925.742857
...
56.0 264450.000000
57.0 58600.000000
58.0 223505.380228
59.0 312725.568627
60.0 223705.490909
Name: median_house_value, Length: 61, dtype: float64
max_iter = 100 / bandwidth = 3 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ========== ===count=== predict 0.0 19998 1.0 64 2.0 8 3.0 132 4.0 2 5.0 2 6.0 192 7.0 1 8.0 1 9.0 1 10.0 6 11.0 3 12.0 1 13.0 1 14.0 1 15.0 2 16.0 6 17.0 1 18.0 8 19.0 1 20.0 1 21.0 1 Name: median_house_value, dtype: int64 ===max=== predict 0.0 500001.0 1.0 500001.0 2.0 500001.0 3.0 500001.0 4.0 293500.0 5.0 258300.0 6.0 500001.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 472700.0 11.0 389700.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 425000.0 16.0 500001.0 17.0 500001.0 18.0 293800.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===median=== predict 0.0 180000.0 1.0 159650.0 2.0 158450.0 3.0 190700.0 4.0 222650.0 5.0 235500.0 6.0 161150.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 248350.0 11.0 124700.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 259400.0 16.0 166950.0 17.0 500001.0 18.0 219650.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===min=== predict 0.0 14999.0 1.0 79900.0 2.0 58600.0 3.0 47500.0 4.0 151800.0 5.0 212700.0 6.0 50000.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 78300.0 11.0 120200.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 93800.0 16.0 86600.0 17.0 500001.0 18.0 53100.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===mean=== predict 0.0 207356.102660 1.0 192432.828125 2.0 215562.625000 3.0 218996.250000 4.0 222650.000000 5.0 235500.000000 6.0 193827.125000 7.0 139300.000000 8.0 152700.000000 9.0 189800.000000 10.0 261350.000000 11.0 211533.333333 12.0 268800.000000 13.0 139200.000000 14.0 442100.000000 15.0 259400.000000 16.0 251333.666667 17.0 500001.000000 18.0 198337.500000 19.0 274500.000000 20.0 131700.000000 21.0 100000.000000 Name: median_house_value, dtype: float64 max_iter = 100 / bandwidth = 4 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ========== ===count=== predict 0.0 20141 1.0 28 2.0 236 3.0 3 4.0 1 5.0 23 6.0 1 Name: median_house_value, dtype: int64 ===max=== predict 0.0 500001.0 1.0 500001.0 2.0 500001.0 3.0 293500.0 4.0 268800.0 5.0 500001.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===median=== predict 0.0 179900.0 1.0 181150.0 2.0 174200.0 3.0 274500.0 4.0 268800.0 5.0 176700.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===min=== predict 0.0 14999.0 1.0 93800.0 2.0 47500.0 3.0 151800.0 4.0 268800.0 5.0 58600.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===mean=== predict 0.0 207294.332357 1.0 234360.785714 2.0 201834.766949 3.0 239933.333333 4.0 268800.000000 5.0 240939.304348 6.0 100000.000000 Name: median_house_value, dtype: float64 max_iter = 300 / bandwidth = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0 9012
1.0 8262
2.0 1739
3.0 109
4.0 70
...
56.0 2
57.0 1
58.0 263
59.0 51
60.0 55
Name: median_house_value, Length: 61, dtype: int64
===max===
predict
0.0 500001.0
1.0 500001.0
2.0 500001.0
3.0 500001.0
4.0 500001.0
...
56.0 343500.0
57.0 58600.0
58.0 500001.0
59.0 500001.0
60.0 500001.0
Name: median_house_value, Length: 61, dtype: float64
===median===
predict
0.0 185700.0
1.0 171700.0
2.0 166000.0
3.0 163300.0
4.0 166100.0
...
56.0 264450.0
57.0 58600.0
58.0 210300.0
59.0 340900.0
60.0 215800.0
Name: median_house_value, Length: 61, dtype: float64
===min===
predict
0.0 14999.0
1.0 14999.0
2.0 22500.0
3.0 53400.0
4.0 47500.0
...
56.0 185400.0
57.0 58600.0
58.0 52800.0
59.0 40000.0
60.0 67900.0
Name: median_house_value, Length: 61, dtype: float64
===mean===
predict
0.0 215848.624723
1.0 198483.938635
2.0 197666.579068
3.0 196085.357798
4.0 188925.742857
...
56.0 264450.000000
57.0 58600.000000
58.0 223505.380228
59.0 312725.568627
60.0 223705.490909
Name: median_house_value, Length: 61, dtype: float64
max_iter = 300 / bandwidth = 3 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ========== ===count=== predict 0.0 19998 1.0 64 2.0 8 3.0 132 4.0 2 5.0 2 6.0 192 7.0 1 8.0 1 9.0 1 10.0 6 11.0 3 12.0 1 13.0 1 14.0 1 15.0 2 16.0 6 17.0 1 18.0 8 19.0 1 20.0 1 21.0 1 Name: median_house_value, dtype: int64 ===max=== predict 0.0 500001.0 1.0 500001.0 2.0 500001.0 3.0 500001.0 4.0 293500.0 5.0 258300.0 6.0 500001.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 472700.0 11.0 389700.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 425000.0 16.0 500001.0 17.0 500001.0 18.0 293800.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===median=== predict 0.0 180000.0 1.0 159650.0 2.0 158450.0 3.0 190700.0 4.0 222650.0 5.0 235500.0 6.0 161150.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 248350.0 11.0 124700.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 259400.0 16.0 166950.0 17.0 500001.0 18.0 219650.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===min=== predict 0.0 14999.0 1.0 79900.0 2.0 58600.0 3.0 47500.0 4.0 151800.0 5.0 212700.0 6.0 50000.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 78300.0 11.0 120200.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 93800.0 16.0 86600.0 17.0 500001.0 18.0 53100.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===mean=== predict 0.0 207356.102660 1.0 192432.828125 2.0 215562.625000 3.0 218996.250000 4.0 222650.000000 5.0 235500.000000 6.0 193827.125000 7.0 139300.000000 8.0 152700.000000 9.0 189800.000000 10.0 261350.000000 11.0 211533.333333 12.0 268800.000000 13.0 139200.000000 14.0 442100.000000 15.0 259400.000000 16.0 251333.666667 17.0 500001.000000 18.0 198337.500000 19.0 274500.000000 20.0 131700.000000 21.0 100000.000000 Name: median_house_value, dtype: float64 max_iter = 300 / bandwidth = 4 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ========== ===count=== predict 0.0 20141 1.0 28 2.0 236 3.0 3 4.0 1 5.0 23 6.0 1 Name: median_house_value, dtype: int64 ===max=== predict 0.0 500001.0 1.0 500001.0 2.0 500001.0 3.0 293500.0 4.0 268800.0 5.0 500001.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===median=== predict 0.0 179900.0 1.0 181150.0 2.0 174200.0 3.0 274500.0 4.0 268800.0 5.0 176700.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===min=== predict 0.0 14999.0 1.0 93800.0 2.0 47500.0 3.0 151800.0 4.0 268800.0 5.0 58600.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===mean=== predict 0.0 207294.332357 1.0 234360.785714 2.0 201834.766949 3.0 239933.333333 4.0 268800.000000 5.0 240939.304348 6.0 100000.000000 Name: median_house_value, dtype: float64 max_iter = 500 / bandwidth = 2 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ==========
===count===
predict
0.0 9012
1.0 8262
2.0 1739
3.0 109
4.0 70
...
56.0 2
57.0 1
58.0 263
59.0 51
60.0 55
Name: median_house_value, Length: 61, dtype: int64
===max===
predict
0.0 500001.0
1.0 500001.0
2.0 500001.0
3.0 500001.0
4.0 500001.0
...
56.0 343500.0
57.0 58600.0
58.0 500001.0
59.0 500001.0
60.0 500001.0
Name: median_house_value, Length: 61, dtype: float64
===median===
predict
0.0 185700.0
1.0 171700.0
2.0 166000.0
3.0 163300.0
4.0 166100.0
...
56.0 264450.0
57.0 58600.0
58.0 210300.0
59.0 340900.0
60.0 215800.0
Name: median_house_value, Length: 61, dtype: float64
===min===
predict
0.0 14999.0
1.0 14999.0
2.0 22500.0
3.0 53400.0
4.0 47500.0
...
56.0 185400.0
57.0 58600.0
58.0 52800.0
59.0 40000.0
60.0 67900.0
Name: median_house_value, Length: 61, dtype: float64
===mean===
predict
0.0 215848.624723
1.0 198483.938635
2.0 197666.579068
3.0 196085.357798
4.0 188925.742857
...
56.0 264450.000000
57.0 58600.000000
58.0 223505.380228
59.0 312725.568627
60.0 223705.490909
Name: median_house_value, Length: 61, dtype: float64
max_iter = 500 / bandwidth = 3 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ========== ===count=== predict 0.0 19998 1.0 64 2.0 8 3.0 132 4.0 2 5.0 2 6.0 192 7.0 1 8.0 1 9.0 1 10.0 6 11.0 3 12.0 1 13.0 1 14.0 1 15.0 2 16.0 6 17.0 1 18.0 8 19.0 1 20.0 1 21.0 1 Name: median_house_value, dtype: int64 ===max=== predict 0.0 500001.0 1.0 500001.0 2.0 500001.0 3.0 500001.0 4.0 293500.0 5.0 258300.0 6.0 500001.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 472700.0 11.0 389700.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 425000.0 16.0 500001.0 17.0 500001.0 18.0 293800.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===median=== predict 0.0 180000.0 1.0 159650.0 2.0 158450.0 3.0 190700.0 4.0 222650.0 5.0 235500.0 6.0 161150.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 248350.0 11.0 124700.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 259400.0 16.0 166950.0 17.0 500001.0 18.0 219650.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===min=== predict 0.0 14999.0 1.0 79900.0 2.0 58600.0 3.0 47500.0 4.0 151800.0 5.0 212700.0 6.0 50000.0 7.0 139300.0 8.0 152700.0 9.0 189800.0 10.0 78300.0 11.0 120200.0 12.0 268800.0 13.0 139200.0 14.0 442100.0 15.0 93800.0 16.0 86600.0 17.0 500001.0 18.0 53100.0 19.0 274500.0 20.0 131700.0 21.0 100000.0 Name: median_house_value, dtype: float64 ===mean=== predict 0.0 207356.102660 1.0 192432.828125 2.0 215562.625000 3.0 218996.250000 4.0 222650.000000 5.0 235500.000000 6.0 193827.125000 7.0 139300.000000 8.0 152700.000000 9.0 189800.000000 10.0 261350.000000 11.0 211533.333333 12.0 268800.000000 13.0 139200.000000 14.0 442100.000000 15.0 259400.000000 16.0 251333.666667 17.0 500001.000000 18.0 198337.500000 19.0 274500.000000 20.0 131700.000000 21.0 100000.000000 Name: median_house_value, dtype: float64 max_iter = 500 / bandwidth = 4 Done.
<Figure size 432x288 with 0 Axes>
========== Compare with original labels ========== ===count=== predict 0.0 20141 1.0 28 2.0 236 3.0 3 4.0 1 5.0 23 6.0 1 Name: median_house_value, dtype: int64 ===max=== predict 0.0 500001.0 1.0 500001.0 2.0 500001.0 3.0 293500.0 4.0 268800.0 5.0 500001.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===median=== predict 0.0 179900.0 1.0 181150.0 2.0 174200.0 3.0 274500.0 4.0 268800.0 5.0 176700.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===min=== predict 0.0 14999.0 1.0 93800.0 2.0 47500.0 3.0 151800.0 4.0 268800.0 5.0 58600.0 6.0 100000.0 Name: median_house_value, dtype: float64 ===mean=== predict 0.0 207294.332357 1.0 234360.785714 2.0 201834.766949 3.0 239933.333333 4.0 268800.000000 5.0 240939.304348 6.0 100000.000000 Name: median_house_value, dtype: float64